I've been working with a dataset that has proven difficult to work with. I'm trying to determine what environmental variables are associated with catch of a rare species of fish (which has been sampled at different locations over 15 years or so). Because the population has declined over the years, I've had Sampling location (or station) nested within year. In order to deal with overdispersion (for zero inflated data, as there's a lot of sampling where nothing was caught), I also included an observation level random effect.
When I run the model with just one fixed effect in the model (Volume sampled), the model works fine. However, when I plug in other variables, I get the error 'Warning message: In mer_finalize(ans) : gr cannot be computed at initial par (65)'. I'm not quite sure what this means, and what I should do to deal with this error. There are a lot of NAs in the data, so I'm not sure if that is contributing to this issue. I haven't had much luck troubleshooting this problem, so any help you can provide would be much appreciated.
I've attached my dataset, and included below are the details for what I've done, and the problems I've come across.
Thanks, Kris
##Here's the model with only one fixed term included:
Catch5 <- lmer(Juv_Catch ~ Volume + (1|Yr/Station2) + (1|obs) , family = poisson, data = na.omit (dataB)) Number of levels of a grouping factor for the random effects
is *equal* to n, the number of observations
##I get this error, but I've read that this is fine when including an observation level random
##When I try including additional variables, I get the error described in the subject
Catch6 <- lmer(Juv_Catch ~ Volume + Secchi + (1|Yr/Station2) + (1|obs) , family = poisson, data = na.omit (dataB)) Number of levels of a grouping factor for the random effects
is *equal* to n, the number of observations
Warning message:
In mer_finalize(ans) : gr cannot be computed at initial par (65)
##The same error comes up when putting in other variables as well (so it doesn't seem to be just that variable). Ideally, I'd like to test for the full model below (or at least compare different modes using AIC)
Catch <- lmer(Juv_Catch ~ Volume + Secchi + EcTop + WaterTemp + AvgD_2 + HoleD_2 + Channel_Wdth + Velocity2 + (1|Yr/Station2) + (1|obs) , family = poisson, data = (dataB)) ##If anyone can help shed some light as to what's going on and why I can't run the model, I'd REALLY appreciate it. Thanks in advance for your help!
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